Calculating models fairness.

I am going to calculate models fairness. I use adult income dataset. Models that I use are Random Forest Regressor and Logistic Regression.

First model that I use is RandomForestClassifier. I have already used RandomForestRegressor before. Difference is that Classifier outputs probability distribution over classes, not a single number. Later I apply bias mitigation methode 'roc-pivot' to the explainer.

                          recall  precision        f1  accuracy       auc
RandomForestClassifier  0.253273   0.896698  0.394983   0.81434  0.883648

                          recall  precision        f1  accuracy       auc
RandomForestClassifier  0.177719   0.982033  0.300971  0.802465  0.883782

Found NaN's or 0's for models: {'RandomForestClassifier'}
It is advisable to check 'metric_ratios'

Logistic regression aims to solve classification problems. It does this by predicting categorical outcomes, unlike linear regression that predicts a continuous outcome.

                      recall  precision        f1  accuracy       auc
LogisticRegression  0.330966   0.599597  0.426508  0.787028  0.809132

3. These coefficients differ between models, we can see many too low values for first one while same coefficients are too high or match norms in a second model.
4. After applying roc-pivot, there is no improvement, I have tried also with reweight, but it did not work either.
5. Based on these models I do not see correletion, all models are highly unfair.